CODE 106847 ACADEMIC YEAR 2022/2023 CREDITS 6 cfu anno 2 ECONOMICS AND DATA SCIENCE 11267 (LM-56) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR MAT/08 TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW These lectures provide the students with the comprehension of the main conceptual and computational tools concerned with the interpretation of big amount of data with predictive purposes. Specifically, as far as the data analysis is concerned, the lectures will describe the crucial aspects related to the processing of time series, introduce the Bayesian analysis and filtering, and provide the basics of pattern recognition. The second part of the lectures will be devoted to discuss the main predictive approaches, including regularization theory, machine and deep learning. The teaching approach will combine theoretical aspectes with focus on applications in economics and other applied sciences AIMS AND CONTENT LEARNING OUTCOMES The aim of these lectures is to provide students with a fair understanding of the main conceptual and computational tools concerned with the interpretation of big amount of data and with the use of such data for predictive purposes. AIMS AND LEARNING OUTCOMES The first part of the course involves the crucial aspected related to the processing of time series, the Bayesian analysis and filtering and the basics of pattern recognition. The second part will discuss the main predictive approaches, such as regularization theory, machine and deep learning. The teaching approach will combine the description of the main theoretical aspects of data analysis with some focus on applications in economics and applied sciences At the end of the course the students will gain some insights in the computational data analysis world and in the comprehension of some aspects of artificial intelligence. Further, they will obtain skills concerning the computational tools for the processing of data and time series with predictive purposes PREREQUISITES R or Matlab programming Data formats and I/O issues Basic aspects of numerical analysis and statistics TEACHING METHODS Frontal teaching and computer projects SYLLABUS/CONTENT Data analysis: time series Bayesian filtering Pattern recognition Prediction from data: regularization theory neural networks Bayesian approaches deep learning RECOMMENDED READING/BIBLIOGRAPHY Handouts TEACHERS AND EXAM BOARD MICHELE PIANA Ricevimento: Office hours by appointment via email FEDERICO BENVENUTO Exam Board FEDERICO BENVENUTO (President) MICHELE PIANA (President) LESSONS LESSONS START To be decided Class schedule L'orario di tutti gli insegnamenti è consultabile all'indirizzo EasyAcademy.